The association of regional cerebral blood flow and glucose metabolism in normative ageing and insulin resistance

Rising rates of insulin resistance and an ageing population are set to exact an increasing toll on individuals and society. Here we examine the contribution of age and insulin resistance to the association of cerebral blood flow and glucose metabolism; both critical process in the supply of energy for the brain. Thirty-four younger (20–42 years) and 41 older (66–86 years) healthy adults underwent a simultaneous resting state MR/PET scan, including arterial spin labelling. Rates of cerebral blood flow and glucose metabolism were derived using a functional atlas of 100 brain regions. Older adults had lower cerebral blood flow than younger adults in 95 regions, reducing to 36 regions after controlling for cortical atrophy and blood pressure. Lower cerebral blood flow was also associated with worse working memory and slower reaction time in tasks requiring cognitive flexibility and response inhibition. Younger and older insulin sensitive adults showed small, negative correlations between relatively high rates of regional cerebral blood flow and glucose metabolism. This pattern was inverted in insulin resistant older adults, who showed hypoperfusion and hypometabolism across the cortex, and a positive correlation. In insulin resistant younger adults, the association showed inversion to positive correlations, although not to the extent seen in older adults. Our findings suggest that the normal course of ageing and insulin resistance alter the rates of and associations between cerebral blood flow and glucose metabolism. They underscore the criticality of insulin sensitivity to brain health across the adult lifespan.


Demographic and Cognitive Battery
Prior to the scan, participants completed an online demographic and lifestyle questionnaire including age, sex, education, height and weight, history of smoking, alcohol and recreational drug use.Participants also completed a cognitive test battery consisting of measures of general intelligence, working memory, cognitive flexibility, inhibitory control and verbal learning.
Wechsler Abbreviated Scale of Intelligence (WASI-IQ).An assessment of intelligence suitable for ages 6-90 years [1].There are 4 subtests: block design, vocabulary, matrix reasoning and similarities.WASI-IQ was scored by converting raw scores into a scale score, which were transformed into a composite score reflecting verbal comprehension and perceptual reasoning abilities (FSIQ2).This score was converted to an age-based T scores established in a normal population.

Hopkins Verbal Learning Test (HVLT).
A three-trial list learning and free recall task comprising 12 words, four words from each of three semantic categories [2].Approximately 20-25 minutes later, a delayed recall trial and a recognition trial was completed.The delayed recall required free recall of any words remembered.The recognition trial comprised 24 words, including the 12 target words and 12 false-positives, six semantically related, and six semantically unrelated.Delayed recall (total words recalled) and a recognition discrimination index (number of correct minus number of false positives in the recognition task) were calculated.Digit Span.A measure of verbal short term and working memory used in two formats: Forward and backward digit span [3].Participants were presented with a series of digits, and are asked to repeat them in either the order presented (forward span) or in reverse order (backwards span).After two consecutive failures of the same length, the test was stopped.Scores were derived as the length of longest correct series for both forward and backward recall.
Task Switching.A computer-based test in which participants were given a word and had to perform one of two simple categorisation tasks, depending on the cue that appeared with the word: 1) 'living' task.If the cue was a heart, participants were asked to categorise the word via a key press based on whether it represents a LIVING versus a NON-LIVING object; and 2) 'size' task.If the cue was an arrow-cross, participants were asked to categorise the word via a key press based on whether it represents an object that is BIGGER or SMALLER than a basketball.The cue selection for each new trial was randomised.Half the test trials were switch trials; half non-switch trials.Half the switch and non-switch trials were congruent in the key presses for either task, half were incongruent.The measures used included the mean latency of correctly responding to a switch trial and switch cost.Switch cost is the difference between mean correct latency of switch trials and nonswitch trials with positive value indicating participants were slower on switch trials, that is, there was a latency cost to switching [4].
Stop Signal.A computer-based test in which participants were presented an arrow that pointed either right or left [5].The task was to press the left response key if the arrow pointed to the left and press the right response key if the arrow pointed to the right, unless a signal beep is played after the presentation of the arrow.In this case the response should be stopped before execution.The delay between presentation of arrow and signal beep (starting at 250ms) was adjusted up or down (by 50ms) depending on performance.The delay got longer if the previous signal stop was successful (up to 1150ms) and smaller if the previous signal stop was not successful (down to 50ms).The stimulus onset asynchrony between the start of each trial (onset of fixation circles) was 2000ms.Variables were the mean reaction time in stop signal trials and stop signal reaction time.Stop signal reaction time is an estimate of inhibition ability, that is, the time required to stop the initiated goprocess.The slower the stop signal reaction time, the more difficult to stop the go-process.

Digit Symbol Substitution.
A computer-based task in which participant were presented with an 18 columns x 16 rows matrix [6].The task was to translate symbols shown above the matrix (key) into digits in the matrix within a two minute period.Total count of correct responses and seconds per correct response were recorded.

The Effect of Age, Cortical
Thickness and Blood Pressure on Regional CBF Figure S1.Mean and standard deviation (error bars) of regional cerebral blood flow for younger and older adults.The data are also presented in Table S1.S3.Table S4.General linear models of the association between regional cerebral blood flow among the four groups based on age category and insulin resistance levels (HOMA-IR median split), with blood pressure and cortical thickness as covariates.Post-hoc contrasts in the 40 regions with significant group difference are shown, comparing the young insulin sensitive group to the other three groups.The group effect sizes are plotted on the brain surface in Figure 2 in main document.Table S5.General linear models of the association between regional cerebral blood flow among the four sub-groups based on age category and HOMA-IR2 levels, with blood pressure and cortical thickness as covariates.Post-hoc contrasts in the 42 regions with significant group difference are shown, comparing the young insulin sensitive group to the other three groups.The location of the 40 regions is plotted on the brain surface in Figure 2    -   and older high GLU.The data for each group are also plotted on the brain surface in Figure 2 in main document.Median split at 5.0 for GLU.The negative correlations for younger high GLU group are not significant due to small sample size.

Figure S4 .
Fig. Partial Correlation of Cerebral Blood Flow and Glucose Metabolism (Controlling Cortical Thickness and Systolic Blood Pressure) in Schaefer 100 Regions for four groups of participants: younger, low GLU; younger high GLU; older low GLU; and older high GLU.The data for each group are also plotted on the brain surface in Figure 2 in main document.Median split at 5.0 for GLU.The negative correlations for younger high GLU group are not significant due to small sample size.-1.00 -0.50 0.00 0.50 1.00 Temporal Parietal 3 Temporal Parietal 2 Temporal Parietal 1 Default C: Parahippocampal Cortex 1 Default C: Retro Superior Parietal… Default B: Ventral Pref rontal Cortex 2 Default B: Ventral Pref rontal Cortex 1 Default B: Dorsal Prefrontal Cortex 1 Default A: Medial Prefront al Cortex 1 Default A: Precuneus Posterior Cingulate… Default A: Dorsal Prefrontal Cortex 1 Default A: I nferior Parietal Lobule 1 Control C: Precuneus 1 Control C: Cingulate Posterior 1 Control B: Lateral Prefrontal Cortexv 1 Control B: Lateral Prefrontal Cortexd 1 Control B: inferior parietal lobule 1 Control B: Temporal 1 Control A: Lateral Prefrontal Cort ex 2 Control A: Lateral Prefrontal Cort ex 1 Control A: Intraparietal Sulcus 1

Fig.
Fig. Partial Correlation of Cerebral Blood Flow and Glucose Metabolism (Controlling ALL DEMOS) in Schaefer 100 Regions for four groups of participants: younger, low GLU; younger high GLU; older low GLU;and older high GLU.The data for each group are also plotted on the brain surface in Figure2in main document.Median split at 5.0 for GLU.The negative correlations for younger high GLU group are not significant due to small sample size.

Table S2 .
General liner models of the association of regional cerebral blood flow with age category (model 1), age category and cortical thickness (model 2) and age category, cortical thickness and blood pressure (model 3).The age category effect sizes from each model are plotted on the brain surface in Figure1in main document.

2 The Effect of Age and Insulin Resistance on Regional CBF
Continued … Figure S2.Mean and standard deviation (error bars) of regional cerebral blood flow for four groups based on age group and median HOMA-IR split.The data is also presented in Table

Table S3 .
Mean and standard deviation (SD) of regional cerebral blood flow (ml/100g/min) among the four subgroups based on age group and HOMA-IR median split.The data is also displayed in FigureS2.

The Effect of Age and Insulin Resistance on Regional CBF-CMRGLU Associations
in main document.

Table S6 .
Mean and standard deviation (SD) of cerebral metabolic rates of glucose (mg/100ml/min) among the four subgroups based on age and HOMA-IR.
Figure S3.Partial correlation of regional CBF and CMRGLC controlling cortical thickness and systolic and diastolic blood pressure for four groups of participants based on age category and HOMA-IR levels: younger insulin sensitive; younger insulin resistant; older insulin sensitive; and older insulin resistant.The data for each group are also plotted on the brain surface in Figure4in main document.

Table S7 .
General linear models of the association between regional cerebral blood flow and age category, blood pressure, cortical thickness, resting heart rate, BMI, sex, years of education.

Table S8 .
General linear models of the association between cerebral blood flow among the four sub-groups based on age category and HOMA-IR levels, with blood pressure, cortical thickness, resting heart rate, BMI, sex, years of education as covariates.Post-hoc contrasts in the regions with significant group difference are shown, comparing the young insulin sensitive group to the other three groups.FigureS5.Partial correlation of regional CBF and CMRGLC controlling for cortical thickness blood pressure, resting heart rate, BMI, sex and years of education.Four groups based on age category and HOMA-IR levels: younger insulin sensitive; younger insulin resistant; older insulin sensitive; and older insulin resistant YIS = younger insulin sensitive; YIR = younger insulin resistant; OIS = older insulin sensitive; OIR = older insulin resistant